Passive vehicle classification using low frequency electro-magnetic emanations

ABSTRACT

A system for passively sensing low frequency (5 Hz to 50 kHz) EM emanations from vehicles and classifying them on that basis. A temporal frequency response is computed for the sensed EM emanations. Harmonic, non-harmonic and temporal features are extracted from the response and used to classify the vehicle. In the preferred embodiment, the temporal frequency response is displayed as a time, frequency and intensity plot, from which a technician visually extracts these features. The features can then be used either to determine a &#34;signature&#34; for classifying the vehicle or to determine specific characteristics which, in turn, can be used to classify the vehicle. Alternately, the feature extraction and classification can be performed by an automated classifier.

This invention was made with Government support under Contract No.N66001-94-C-6029 awarded by the Department of the Navy. The Governmenthas certain rights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to passive vehicle classification and morespecifically to a system and method for passively sensing a vehicle'slow frequency electro-magnetic (EM) emanations and identifying harmonicinterrelationships, strong non-harmonic signals, and temporal propertiesto classify the vehicle.

2. Description of the Related Art

Passive vehicle detection and classification systems rely on passivelysensed visual, thermal, seismic and acoustical data to first detect andthen classify vehicles. Technicians are trained to recognize patterns inthe sensed data as particular vehicle characteristics and then use themto classify the vehicle. Depending upon the application, the type ofvehicles involved, and the environment in which the detection takesplace, it might not be practical to gather certain types of data. Thisreduces the total amount of data that is available to the technician toclassify the vehicle. Furthermore, the sensed data may be very noisy dueto sensor characteristics, environmental conditions or other sources inthe area. Lastly, each type of data can characterize only certainaspects of the vehicle. As a result, the sensed data may notcharacterize the vehicle with sufficient specificity to classify it withthe desired confidence and precision.

The SURTASS system developed by Hughes Electronics, the assignee of thepresent invention, uses one or more acoustic sensors to passively senseacoustic emanations from passing submarines. No other types of data aregathered and used in the classification process. The acoustic data ispassed to a computer that filters the data and then computes itstemporal frequency response or spectrogram, commonly known as a LOwFrequency Analysis Response (LOFARgram), which is displayed as atime-frequency-intensity plot on a terminal. The technician is trainedto recognize distinctive acoustic patterns in the LOFARgram and classifythe submarine.

The VLF Home Page found on the Internet at dlc@nov a.stanford.edu asupdated on Jun. 13, 1995 presented a discussion of lightning-generatedwhistlers in the Earth's geomagnetic field. A network of low frequency(0-10 kHz) magnetic sensors are used to detect the magnetic emanationsassociated with lightning. These measurements are typically performed inremote locations in the Northern Hemisphere or Antarctica to avoid othersources of low frequency magnetic signals such as the strong 60 Hzsignal associated with local power grids or the low frequency emanationsgenerated by vehicles, which would appear as noise and tend to obscurethe lightning. To observe and study the lightning-generated whistlers,the sensed magnetic signals are first mapped into audio signals thatsound like decaying whistles and displayed in coordinates of frequencyversus time, with the intensity indicated by the color of the display.This illustrates what lightning looks and sounds like in the lowfrequency spectrum.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention provides a systemfor passively sensing EM emanations from vehicles and classifying themon that basis.

This is accomplished by sensing the low frequency EM emanations between5 Hz and 50 kHz from a vehicle, computing a temporal frequency response,extracting harmonic, non-harm onic and temporal features from theresponse, and using them to classify the vehicle. In the preferredembodiment, the temporal frequency response is displayed as a time,frequency and intensity plot, from which a technician visually extractsthese features. The features can then be used either to determine a"signature" for classifying the vehicle or to determine specificcharacteristics which, in turn, can be used to classify the vehicle.Alternately, the feature extraction and classification can be performedby an automated classifier.

These and other features and advantages of the invention will beapparent to those skilled in the art from the following detaileddescription of preferred embodiments, taken together with theaccompanying drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for classifying EM vehicles inaccordance with the present invention;

FIG. 2 is a flow chart illustrating the feature extraction andclassification of vehicles in FIG. 1;

FIG. 3 is a plot of an EM spectrogram illustrating the harmonicinterrelationships produced by a vehicle's combustion engine, which areindicative of the number of cylinders in the engine, and a strongnon-harmonic component produced by a vehicle's alternator, which isindicative of the alternator's pulley ratio;

FIG. 4 is a plot of an EM spectrogram illustrating the temporal responseof the ignition frequency and its harmonics for a manual transmissionvehicle when the vehicle is accelerating; and

FIG. 5 is a plot of an EM spectrogram illustrating the temporal responseof the ignition frequency and its harmonics for an automatictransmission vehicle when the vehicle is accelerating.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a passive system for classifying vehiclesbased upon their low frequency electro-magnetic (EM) emanations (5-50kHz) either independently or in conjunction with known visual, thermal,seismic or acoustic classification systems. When used in conjunctionwith these other systems, the inclusion of EM data increases theredundancy of information from which certain vehicle characteristics canbe determined and provides additional characteristics unique to EMdetection.

It is readily known that vehicles such as submarines, cars, trucks, andplanes generate low frequency EM signals. However, conventional wisdomis to treat these EM signals as a source of noise that cannot be used toclassify vehicles, and thus should be either avoided or filtered. Thetreatment of vehicle EM emanations as noise is due in part to the lackof characterization data about particular vehicles and the insensitivityof off-the-shelf EM sensors to these low frequency emanations. Thesefactors combined with the amount of low frequency harmonic and transientnoise present in the environment caused people in the field to believethat it would be impractical to detect vehicle emanations and use themto classify the vehicle with a sufficiently high degree of confidenceand precision.

Applicant has discovered that features such as harmonicinterrelationships, strong non-harmonic components and temporalproperties of identifiable signals can in fact be detected and extractedfrom the vehicle's temporal frequency response (spectrogram) and usedeither to directly discern a vehicle signature or to extract specificcharacteristics from which the vehicle can be classified. Adequateperformance has been realized using known EM sensors and rudimentaryvehicle characterizations in the presence of strong harmonic andtransient noise signals. Applicant expects the classificationperformance to increase as more complete and accurate characterizationdata is developed and as more sensitive EM sensors are designed for theparticular purpose of sensing vehicle EM emanations.

FIG. 1 illustrates a passive EM vehicle classification system 10 forclassifying vehicles 12 based upon their low frequency EM emanations.The vehicles' many different electrical and mechanical systems generatelow frequency EM signals between 5 Hz and 50 kHz at varying intensitylevels. The car depicted in FIG. 1 includes a 4-cycle combustion engine14 that fires a plurality of cylinders 16 at an ignition frequency F_(I)to rotate a crankshaft 18 that is engaged through a transmission 20 topower the car. The car emits relatively weak EM signals at thecrankshaft's rotation frequency F_(C) and its harmonics and relativelystrong EM signals at the ignition frequency F_(I) and its harmonics. Theharmonic interrelationship of these signals are indicative of the numberof cylinders N, for example, 4,6 or 8. As the car changes speed, thecrankshaft and ignition frequencies will exhibit a temporal responsethat is characteristic of the type of transmission; manual or automatic.The car's alternator 22 is coupled to the crankshaft 18 by a pulley 24,which causes a d-pole rotor 26 inside the alternator to rotate andproduce a strong electrical signal at a non-harmonic alternatorfrequency F_(A). The pulley ratio, which is indicative of differenttypes of cars, can be determined from the interrelationship of thecrankshaft and alternator frequencies. Further testing that improves thecharacterization of the vehicle and improved EM sensors are expected toreveal other systems such as the vehicle's lighting system that emanatedistinctive low frequency EM signals that can be used to classify thevehicle.

The passive EM vehicle classification system 10 includes at least one EMsensor 28 that couples the EM signals produced by the vehicle, the localpower grid, and lightning into at least one electrical signal that isamplified by a pre-amplifier 29. The EM sensors 28 may include a coil 30that is wrapped around a magnetically permeable core 32 for couplingmagnetic signals or an antenna 34 for coupling electrical signals. Animportant practical advantage, is that EM sensors, unlike optical oracoustic sensors, can be hidden or buried.

In the system that was used to produce the spectrograms shown in FIGS.3-6, the EM sensor 28 included a pair of 3-axis search coils that wereassembled from off-the-shelf relay coils. The sensitivity of the coils,and thus the classification performance, may be improved by designingthem to detect specific frequency bands associated with known vehiclesystems. In addition, the coils may be designed to remove the strong 60Hz signal component, thereby allowing greater pre-amplification.

However, since currently available EM sensors do not remove the 60 Hzsignal, a noise removal filter 36 is used to reject the 60 Hz component.This can be done in a number of ways including subtracting the EM signalthat is detected when no vehicle is present from the measured EM signalsor by using the filter described in U.S. Pat. No. 5,425,105 entitled"Multiple Adaptive Filter Active Noise Canceler." The same techniquescan also be used to remove other known sources of noise. The filteredsignal is then passed to a noise whitening filter 38 that is designed sothat the power spectral density of the filtered signal is approximatelyflat over the frequency range when collecting only background EMemanations in the absence of a vehicle.

The filtered signal is then passed to a processor 40 that computes itstemporal frequency response. This is preferably done using a commercialfast fourier transform (FFT) routine, such as is found in Matlab, thatoperates on samples of the filtered signal. The routine's window size(number of samples) is selected to define a bin size (frequency range)that provides adequate frequency resolution of the detected EM signals,suitably 1-10 Hz. A transient filter 42 low pass filters the signals inthe temporal frequency response associated with each of the bins toremove the noise produced by transient EM sources such as lightning.

The temporal frequency response is then passed to a processing unit 44that is capable of interpreting the response, extracting salientfeatures of detectable vehicles in the vicinity of the EM sensor, andclassifying the vehicle as described in detail in FIG. 2. In thecurrently preferred embodiment, the processing unit includes a display46 that displays the temporal frequency response as atime-frequency-intensity plot 48, in which time is plotted on thehorizontal axis, frequency is plotted on the vertical axis, andintensity is represented by the color or gray-scale shade of the plot. Ahuman technician 50, who is trained to recognize harmonic, non-harmonicand temporal patterns in the plot, observes or extracts distinguishingfeatures and used them to classify the vehicle 12.

Alternately, the processing unit 44 may include an automated classifier52 in place of or in addition to the human technician 50. The automatedclassifier 52 includes a feature extractor 54 that is designed toidentify and extract distinctive features in the temporal frequencyresponse such as harmonic interrelationships, strong non-harmonicsignals, and temporal properties. A classifier 56 such as a Bayesian orNeural Net classifier processes the extracted features to classify thevehicle. Typically, the classifier 56 is trained off-line with EM signaldata from known vehicles until it learns to associate certain featureswith the different vehicles. This can be done in a single step, in whichthe classifier maps all of the feature data to the classified data, orin two steps, in which the classifier first maps the feature data intospecific characteristics such as the number of cylinders, pulley ratioand transmission type and then uses those characteristics to classifythe vehicle.

Although the principles underlying feature extraction and classificationalgorithms are well known and used extensively in signal processingapplications, the use of an automated classifier in this particularproblem is currently beyond the state-of-the-art, i.e. theclassification results are not accurate enough to use in practice.Although the current characterization of different vehicles issufficient to allow a trained technician to detect and identify a fewsystems, the characterization is not complete enough to allow a computerto perform the same function. The different vehicles must be isolatedand tested to accurately identify the systems and the EM signals theyproduce. This data will then be used to design the feature extractor 54and train the classifier 56. Secondarily, humans are better able toidentify and extract the desired signals from noise than are computers.Thus, EM sensors that are specially designed to detect the EM signalsemanated by vehicles will improve the performance of the automatedclassifier.

FIG. 2 illustrates the steps performed by the technician, or analogouslyby the automated classifier, shown in FIG. 1 to classify the vehicle.The technician views the displayed spectrogram (step 58) and performseither a characteristic specific analysis or a "signature" analysis onthe spectrogram (step 60) to classify the vehicle. In a characteristicspecific analysis, the technician identifies and extracts specificfeatures from the spectrogram such as harmonic signal patterns,strong-non harmonic signals, and temporal properties (step 62) that thetechnician knows from experience correspond to specific vehicle systems.Thereafter, the technician uses these features to determinedcharacteristic values such as the number of cylinders or the pulleyratio or types such as manual or automatic transmission (step 64). Thetechnician then matches these characteristics against a library of knowncharacteristics for different vehicles to classify the vehicle (step66). The classification precision will depend upon how many differentcharacteristics are identified and how unique they are. For example,classification may be as simple as identifying whether the vehicle iscivilian or military or as complex as identifying the specific make andmodel of a vehicle.

In a signature analysis, the technician observes but does not extractspecific features (step 68). Together the observed features represent asignature of the vehicle (step 70). The technician then matches theobserved signature against a library of signatures to classify thevehicle (step 72). An experienced technician may recognize the observedsignature as a specific class of vehicles or the specific make and modelwithout explicitly matching it against the known signatures.

FIG. 3 is a plot 74 of the spectrogram generated by a 4-cylinder vehiclehaving a hand-measured alternator pulley ratio of approximately 2.45. Ina standard 4-cycle combustion engine having 4 cylinders, an ignitionpulse is emitted twice for every revolution of the engine. This signalis rich in harmonics, which show up on the spectrogram as a set ofequally spaced lines 76. In this case, the fundamental frequency of thisline is approximately 34 Hz, and has harmonics that appear at every 34Hz thereafter (68 Hz, 102 Hz, 136 Hz, etc.) The ignition frequency isdenoted F_(I) and in this case is 34 Hz.

Another signal is generated that is weaker than the ignition signal, andwhose fundamental frequency is equal to the speed of the crankshaft inthe engine. It too is rich in harmonics, which also appear as a set ofequally spaced lines 78 on the spectrogram. In this example case, thefundamental frequency of this line is approximately 17 Hz, and hasharmonics that appear every 17 Hz thereafter (34 Hz, 51 Hz, 68 Hz, etc.)This is denoted as the crankshaft frequency, or F_(C).

For a standard 4 cycle gasoline engine, the number of cylinders, N, isequal to 2F_(I) /F_(C). In this examplary case, N=2×34 Hz/17 Hz=4. Ahuman operator would determine that N=4 by looking at the spectrogramand observing the strong-weak-strong-weak pattern corresponding to therelationship between F_(I) (strong lines) and F_(C) (weak lines). For a6-cylinder engine, F_(I) =3×F_(C). This would be displayed as astrong-weak-weak-strong-weak-weak pattern on the spectrogram.

A typical automotive alternator (where the number of poles d in thealternator rotor is 6) emits a signal having a frequency of 6 times itsrotational speed. The frequency of this signal is denoted F_(A). Thespeed of the alternator is in turn, equal to r_(P) ×F_(C), where r_(P)is the ratio of the size of the crankshaft pulley to the size of thealternator pulley. This relationship defines the following equation:

    F.sub.A =d×r.sub.P ×F.sub.C

Thus, the pulley ratio can be solved as follows:

    r.sub.P =F.sub.A /(d×F.sub.C).

Different types of engines from different manufactures will all havepulley ratios that are somewhat different. Thus, this information can bevery useful in classifying a vehicle. Furthermore, most known vehicleshave a pulley ratio that lies between 2 and 3.

Based upon these assumptions, the technician computes a frequency range80 in which to expect a relatively strong intensity level 82 at anon-harmonic frequency that corresponds to the EM signal produced by thealternator. The technician then looks into the frequency range and, ifit exists, extracts the strong signal and equates its frequency toF_(A). If a strong non-harmonic signal is not identified, the techniciancan select the next most popular number of poles d in the rotor andrepeat the process. Once the alternator frequency F_(A) has beendetermined and the number of poles d fixed, the pulley ratio r_(P) iscomputed. In the case shown, a strong non-harmonic component wasidentified at approximately 80% of the way between the 14th and 15thharmonics, 84 and 86, respectively, of the crankshaft frequency, or the7th and 8th harmonics of the ignition frequency, 84 and 88,respectively, for the 4-cylinder vehicle. Because the engine has4-cylinders, the even order harmonics of the crankshaft frequency occurat the same frequency as the harmonics of the ignition frequency. Themeasured non-harmonic component 82 corresponds to a pulley ratio of2.47, which is very close to the hand-measured value of 2.45.

FIGS. 4 and 5 are plots of the spectrograms 90 and 92 generated bymanual and automatic transmission vehicles, respectively. As shown inFIG. 4, the manual transmission vehicle was stopped and then started toaccelerate. The spectrogram 90 reveals two distinctive temporalproperties of a manual transmission vehicle. First, when the driverreleases the clutch, the engine's RPMs dip momentarily, which isreflected as a dip 94 in frequency of the harmonic components associatedwith both the crankshaft and ignition. This is characteristic of theclutch being released in a manual transmission vehicle. Second, becausethe engine speed is directly coupled to the vehicle speed, the rate ofchange 96 of the ignition and crankshaft frequencies is equal to thevehicle's acceleration. If the driver downshifts to decelerate thevehicle, the engine's RPMS will momentarily increase and the rate ofchange of the ignition and crankshaft frequencies will be equal to thedeceleration.

As shown in FIG. 5, the spectrogram 92 of an automatic transmissionvehicle that is accelerating from a stop also reveals two distinctivetemporal properties. First, because there is no clutch to engage, theengine's RPMs do not dip when the vehicle accelerates. Second, theengine's RPMs, and hence the frequency of the harmonic componentsassociated with both the crankshaft and ignition, exhibit a profoundjump 98 as the vehicle is accelerated. Automatic transmissions include atorque converter that trades off engine RPM for torque, therebypermitting the engine to speed up much faster than the vehicle itselfaccelerates. As a result, a technician can monitor the temporalproperties of a strong harmonic component, typically of the ignitionfrequency, when the vehicle is changing speed and use these distinctivefeatures to determine what type of transmission the vehicle has, which,in turn, can be used to classify the vehicle.

While several illustrative embodiments of the invention have been shownand described, numerous variations and alternate embodiments will occurto those skilled in the art. For example, the same techniques can beused to identify features and characteristics of other vehicle systems.Although, the invention was described in conjunction with a 4-cyclecombustion engine it would also be applicable to vehicles that use othersources of power. Such variations and alternate embodiments arecontemplated, and can be made without departing from the spirit andscope of the invention as defined in the appended claims.

We claim:
 1. A method for classifying vehicles that emanate lowfrequency electro-magnetic (EM) signals, comprising:a) passively sensinglow frequency EM signals emanated from a vehicle; b) computing thetemporal frequency response of the EM signals; c) extracting featuresfrom the temporal frequency response; d) using the extracted features todetermine a set of vehicle characteristics; and e) using the set ofvehicle characteristics to classify the vehicle; wherein the vehiclescomprise a 4-cycle combustion engine having a rotating crankshaft and aplurality of cylinders that fire at ignition frequencies F_(I), equal toone-half their crankshaft frequency F_(C) times the number of cylindersN in that vehicle, said engine emanating relatively weak EM signals atthe harmonic frequencies of the crankshaft frequency F_(C) andrelatively strong intensity levels at the harmonic frequencies of theignition frequency F_(I), said harmonic features being extracted byidentifying a first harmonic interrelationship between the relativelyweak signal emanations and a second harmonic interrelationship betweenrelatively strong signal emanations, said first and second harmonicinterrelationships being used to compute the crankshaft and ignitionfrequencies, respectively, from which the number of cylinders N in thevehicle's engine is computed and used to classify the vehicle.
 2. Themethod of claim 1, wherein non-harmonic features are extracted by usinga priori information to first select a frequency range for a particularvehicle characteristic and then identifying a relatively strongintensity level in the temporal frequency response that occurs at anon-harmonic frequency in that frequency range.
 3. The method of claim2, wherein the vehicles comprise a 4-cycle combustion engine having acrankshaft that rotates at a crankshaft frequency F_(C) and analternator with a d-pole rotor that rotates inside the alternator at analternator frequency F_(A), is equal to d×F_(C) ×r_(P) where r_(P) isthe engine's pulley ratio and known to lie in a given range, saidalternator emanating a relatively strong non-harmonic signal at thealternator frequency, said non-harmonic features being extracted byfirst selecting said frequency range using the pulley ratio's givenrange, selecting the relatively strong intensity level in said frequencyrange, and assigning its frequency as the alternator frequency F_(A),from which the pulley ratio is computed.
 4. A method for classifyingvehicles that emanate low frequency electro-magnetic (EM) signals,comprising:a) passively sensing low frequency EM signals emanated from avehicle; b) computing the temporal frequency response of the EM signals;c) extracting features from the temporal frequency response; d) usingthe extracted features to determine a set of vehicle characteristics;and e) using the set of vehicle characteristics to classify the vehicle;wherein harmonic features are extracted by identifying a plurality ofintensity levels in said temporal frequency response that areapproximately uniformly spaced apart in frequency; wherein temporalfeatures are extracted by monitoring the dynamic properties of theextracted harmonic features; and wherein said vehicle comprises a4-cycle engine that fires a plurality of cylinders at an ignitionfrequency F_(I), to rotate a crankshaft that is engaged through atransmission to power the vehicle, said engine emanating strong lowfrequency EM signals at the ignition frequency F_(I) and its harmonics,said temporal features being extracted by detecting momentary dips inthe ignition and harmonic frequencies when the vehicle is acceleratingand determining a rate of change for the frequencies, said temporalfeatures being compared against the characteristics of a manualtransmission, which include momentary dips in frequency when the clutchis engaged and a rate of change that is proportional to the change inthe vehicle's speed, and the characteristics of an automatictransmission, which include a smooth frequency response and a rate ofchange that exceed the change in the vehicle's speed, to characterizethe vehicle's transmission as manual or automatic.
 5. A method forclassifying vehicles that emanate low frequency electro-magnetic (Em)signals, comprising:a) passively sensing low frequency EM signalsemanated from a vehicle between 5 Hz and 50 kHz; b) computing thetemporal frequency response of the EM signals; c) displaying thetemporal frequency response as a time, frequency, intensity plot on adisplay; d) visually extracting features from the time, frequency,intensity plot including harmonic, non-harmonic, and temporalproperties; e) using the extracted features to determine a set ofvehicle characteristics; f) comparing the set of vehicle characteristicsto the known characteristics for a plurality of candidate vehicles toclassify the vehicle; and wherein the vehicle comprise a 4-cyclecombustion engine that fires a plurality of cylinders at an ignitionfrequency F_(I) to rotate a crankshaft that is engaged through atransmission to power the vehicle, said ignition frequency F_(I) beingequal to one-half the crankshaft frequency F_(c) times the number ofcylinders N in the vehicle, said engine emanating relatively weak EMsignals at the harmonic frequencies of the crankshaft frequency F_(c)and relatively strong intensity levels at the harmonic frequencies ofthe ignition frequency F_(I), said harmonic features being extracted byidentifying a first harmonic interrelationship between the relative weaksignal emanations and a second harmonic interrelationship betweenrelatively strong signal emanations, said first and second harmonicinterrelationships being used to compute the crankshaft and ignitionfrequencies, respectively, from which the number of cylinders N in thevehicle's engine is computed and used to classify the vehicle.
 6. Themethod of claim 5, wherein the set of vehicle characteristicscorresponds to a signature for the vehicle, said signature beingcompared to the known signatures for a plurality of candidate vehiclesto classify the vehicle.
 7. The method of claim 5, wherein the vehiclecharacteristics in said set each correspond to a specific system in thevehicle that together characterize the vehicle.
 8. The method of claim5, wherein the vehicles further comprise an alternator with a d-polerotor that rotates inside the alternator at an alternator frequencyF_(A), is equal to d×F_(C) ×r_(P) where r_(P) is the engine's pulleyratio and known to lie in a given range, said alternator emanating arelatively strong non-harmonic signal at the alternator frequency, saidnon-harmonic features being extracted by first selecting said frequencyrange using the pulley ratio's given range, selecting the relativelystrong intensity level in said frequency range, and assigning itsfrequency as the alternator frequency F_(A), from which the pulley ratiois computed.
 9. The method of claim 5, wherein said temporal featuresare extracted by detecting momentary dips in the ignition and harmonicfrequencies when the vehicle is accelerating and determining a rate ofchange for the frequencies, said temporal features being comparedagainst the characteristics of a manual transmission, which includemomentary dips in frequency when the clutch is engaged and a rate ofchange that is proportional to the change in the vehicle's speed, andthe characteristics of an automatic transmission, which include a smoothfrequency response and a rate of change that exceed the change in thevehicle's speed, to characterize the vehicle's transmission as manual orautomatic.
 10. A system for classifying electro-magnetic (EM) vehicles,comprising:a vehicle having a 4-cycle combustion engine that emanateslow frequency EM signals that have distinctive harmonicinterrelationships, strong non-harmonic components, and distinctivetemporal properties; a sensor for passively sensing the low frequency EMsignals from the vehicle; a processor for computing the temporalfrequency response of the EM signals and representing them as a time,frequency, intensity plot; and a display that displays the time,frequency, intensity plot from which a person can observe harmonicinterrelationships, strong non-harmonic components and temporalproperties as features of the vehicle and use them to classify thevehicle.
 11. The system of claim 10, wherein said sensor senses EMsignals in a frequency range from approximately 5 Hz to 50 kHz.
 12. Thesystem of claim 10, wherein said vehicle's 4-cycle combustion engine hasa rotating crankshaft and a plurality of cylinders that fire at anignition frequency F_(I) equal to one-half the crankshaft frequencyF_(C) times the number of cylinders N, said engine emanating relativelyweak EM signals at the harmonic frequencies of the crankshaft frequencyF_(C) and relatively strong EM signals at the harmonic frequencies ofthe ignition frequency F_(I), said display displaying relatively weakintensity levels at the harmonic frequencies of the crankshaft frequencyF_(C) and relatively strong intensity levels at the harmonic frequenciesof the ignition frequency F_(I) from which the person can compute thecrankshaft and ignition frequencies and use them to identify the numberof cylinders N in the vehicle's engine.
 13. The system of claim 12,wherein said combustion engine includes an alternator with a d-polerotor that rotates inside the alternator at an alternator frequencyF_(A), which lies in a known frequency range with respect to thecrankshaft frequency, and is equal to d×F_(C) ×r_(P) where r_(P) is theengine's pulley ratio, said alternator emanating a relatively strongnon-harmonic signal at the alternator frequency, said display displayinga strong non-harmonic intensity level that lies in the known frequencyrange, from which the person can determine the alternator frequencyF_(A) and compute the pulley ratio r_(P).
 14. The system of claim 12,wherein said vehicle further comprises a transmission that exhibitsdifferent temporal ignition frequency characteristics when acceleratingdepending on whether it is an automatic or a manual transmission, saiddisplay displaying the temporal characteristics of the relatively strongintensity levels at the harmonic frequencies of the ignition frequencyF_(I), from which the person can detect momentary dips in the harmonicfrequencies, which is indicative of a manual transmission, and candetermine a rate of change of the harmonic frequencies that exceeds thevehicle's acceleration, which is indicative of an automatictransmission.
 15. The system of claim 10, wherein said temporalfrequency response is resolved into a plurality of frequency bins,further comprising:a transient noise filter that low pass filters the EMsignal in each said frequency bin to reduce the noise associated withtransient EM signals.
 16. The system of claim 15, further comprising anoise reduction filter that pre-whitens the sensed EM signals so thatits power spectral density is approximately flat when the EM sensor issensing background EM signals in the absence of the vehicle.
 17. Asystem for classifying electro-magnetic (EM) vehicles, comprising:asensor for passively sensing low frequency EM signals emanated from avehicle; a processor for computing the temporal frequency response ofthe EM signal; and a classifier that a) extracts features from thetemporal frequency response including harmonic interrelationships of theEM signals, strong non-harmonic EM signals, and temporal properties ofstrong EM signals, b) uses the features to identify knowncharacteristics of possible vehicles, and c) classifies the vehiclebased upon its identified characteristics.
 18. The system of claim 17,wherein said EM vehicle emits EM signals in a frequency range fromapproximately 5 Hz to 50 kHz.
 19. The system of claim 17, wherein saidvehicle comprises a 4-cycle combustion engine having a rotatingcrankshaft and a plurality of cylinders that fire at an ignitionfrequency F_(I) equal to one-half the crankshaft frequency F_(C) timesthe number of cylinders N, said engine emanating relatively weak EMsignals at the harmonic frequencies of the crankshaft frequency F_(C)and relatively strong intensity levels at the harmonic frequencies ofthe ignition frequency F_(I),said classifier a) extracting a firstharmonic interrelationship between the relatively weak signal emanationsand a second harmonic interrelationship between relatively strong signalemanations, b) computing the crankshaft frequency F_(C) from the firstharmonic interrelationship, c) computing the ignition frequency F_(I)from the second harmonic interrelationship, d) using the values of F_(C)and F_(I) to identify the number of cylinders N in the vehicle's engine,and e) using the number of cylinders N to classify the vehicle.
 20. Thesystem of claim 19, wherein said combustion engine includes analternator with a d-pole rotor that rotates inside the alternator at analternator frequency F_(A), which lies in a known frequency range withrespect to the crankshaft frequency, and is equal to d×F_(C) ×r_(P)where r_(P) is the engine's pulley ratio, said alternator emanating arelatively strong non-harmonic signal at the alternator frequency,saidclassifier determining the known frequency range in which thealternator's non-harmonic signal can occur for the computed crankshaftfrequency, extracting a relatively strong component from the temporalfrequency response that occurs at a non-harmonic frequency in said knownfrequency range and assigning the component's frequency as thealternator frequency F_(A), computing the pulley ratio r_(P), and usingit to classify the vehicle.
 21. The system of claim 19, wherein saidvehicle further comprises a transmission that exhibits differenttemporal ignition frequency characteristics when accelerating dependingon whether it is an automatic or a manual transmission,said classifiermonitoring the temporal response of the ignition frequency F_(I) and itsharmonics to detect momentary dips in those frequencies and to determinea rate of change for the harmonic frequencies, said classifier comparingthese characteristics against the known temporal ignition frequencycharacteristics for automatic and manual transmission vehicles toclassify the vehicle's transmission as being automatic or manual. 22.The system of claim 17, wherein said temporal frequency response isresolved into a plurality of frequency bins, further comprising:atransient noise filter that low pass filters the EM signal in each saidfrequency bin to reduce the noise associated with transient EM signals.23. The system of claim 22, further comprising a noise reduction filterthat pre-whitens the sensed EM signals so that its power spectraldensity is approximately flat when the EM sensor is sensing backgroundEM signals in the absence of the vehicle.
 24. The system of claim 17,further comprising:a display that displays the temporal frequencyresponse on a time, frequency, intensity plot that displays the harmonicinterrelationships of the EM signals, the strong non-harmonic EMsignals, and the temporal properties of the strong signals, from which aperson can observe these features, identify known characteristics ofpossible vehicles, and classify the vehicle based upon its identifiedcharacteristics to confirm or reject the classification made by theclassifier.